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Moogsoft AIOps 7.2 Released

Moogsoft released Moogsoft AIOps 7.2, the latest version of its enterprise platform, featuring new capabilities that ease the burden of IT Operations and DevOps teams by optimizing service assurance.

Significant new transparency, efficiency, and customization enhancements include: a new workflow engine, AI visualizations, performance dashboards, and new tool integrations.

New Moogsoft AIOps 7.2 Features

■ New Workflow Engine Manages Workloads, Automates Ticketing & Notifications: Moogsoft AIOps 7.2’s new Workflow Engine provides IT Ops teams the ability to visually create sophisticated custom workflows using a simple but powerful user interface. A rich set of workflow options can trigger actions both within Moogsoft and to external systems for actions such as notifications, ticket creation, and other automated tasks. The Workflow Engine simplifies conditional event processing with enrichment of event alert data, enabling automation of incident management workflows as well as integration with automated remediation tools.

■ Situation Visualization Increases Transparency, Understanding of How Algorithms Work: Situation Visualization provides powerful new visual tools for understanding the operation of Moogsoft’s alert clustering algorithms and, if needed, for fine-tuning them. Similarity clusters are presented as radar charts for each Situation. They provide a window into how the system’s automated decision making works. Users can understand at a glance the matching criteria for those events that have been correlated together into a single Situation. Together with Probable Root Cause, Topology, and other visualizations, Moogsoft’s Situation Room offers real-time situational awareness to IT Ops and DevOps teams.

■ Customization Features Conform to Customers’ Unique Organizational Needs: Moogsoft AIOps 7.2 introduces a number of new features that personalize and configure the platform for a customer’s unique environment and organizational requirements. These comprise:

- Situation Room Headers. The information presented in Situation Room headers can be easily customized to improve operational efficiency. Team members can understand the Situation at a glance and decide on next steps.

- Individual Statistics. A new analytics dashboard called Individual Statistics allows managers to drill down from the team level to better understand the workload and key performance indicators of each individual team member. This insight allows team leaders and all members to optimize work distributions and overall operational effectiveness.

- New Tool Integrations. Moogsoft AIOps platform continues to expand its broad suite of out-of-the-box integrations for faster time to value. New integrations include connectors to New Relic Insights, Microsoft Teams, and proxy support for all polling integrations (e.g. Zenoss, Zabbix, vCenter, vSphere, Solarwinds, Spectrum, and SevOne).

“AIOps is gaining momentum streamlining IT Operations as well as DevOps,” explains Phil Tee, Chairman and CEO of Moogsoft. “We’ve built the AIOps market from the beginning, pioneered the way with over 50 patents, and now help over 120 of the largest corporations transform their IT service assurance. Today we’re delivering the next-generation platform to democratize the use of AIOps at all organizations. Our goal is to make Moogsoft the solution of choice for all enterprises – large and small – for agile, proactive event resolution. To this end, release 7.2 empowers enterprises to avoid outages, meet service level agreements, and accelerate digital transformation.”

The Latest

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

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Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...

Moogsoft AIOps 7.2 Released

Moogsoft released Moogsoft AIOps 7.2, the latest version of its enterprise platform, featuring new capabilities that ease the burden of IT Operations and DevOps teams by optimizing service assurance.

Significant new transparency, efficiency, and customization enhancements include: a new workflow engine, AI visualizations, performance dashboards, and new tool integrations.

New Moogsoft AIOps 7.2 Features

■ New Workflow Engine Manages Workloads, Automates Ticketing & Notifications: Moogsoft AIOps 7.2’s new Workflow Engine provides IT Ops teams the ability to visually create sophisticated custom workflows using a simple but powerful user interface. A rich set of workflow options can trigger actions both within Moogsoft and to external systems for actions such as notifications, ticket creation, and other automated tasks. The Workflow Engine simplifies conditional event processing with enrichment of event alert data, enabling automation of incident management workflows as well as integration with automated remediation tools.

■ Situation Visualization Increases Transparency, Understanding of How Algorithms Work: Situation Visualization provides powerful new visual tools for understanding the operation of Moogsoft’s alert clustering algorithms and, if needed, for fine-tuning them. Similarity clusters are presented as radar charts for each Situation. They provide a window into how the system’s automated decision making works. Users can understand at a glance the matching criteria for those events that have been correlated together into a single Situation. Together with Probable Root Cause, Topology, and other visualizations, Moogsoft’s Situation Room offers real-time situational awareness to IT Ops and DevOps teams.

■ Customization Features Conform to Customers’ Unique Organizational Needs: Moogsoft AIOps 7.2 introduces a number of new features that personalize and configure the platform for a customer’s unique environment and organizational requirements. These comprise:

- Situation Room Headers. The information presented in Situation Room headers can be easily customized to improve operational efficiency. Team members can understand the Situation at a glance and decide on next steps.

- Individual Statistics. A new analytics dashboard called Individual Statistics allows managers to drill down from the team level to better understand the workload and key performance indicators of each individual team member. This insight allows team leaders and all members to optimize work distributions and overall operational effectiveness.

- New Tool Integrations. Moogsoft AIOps platform continues to expand its broad suite of out-of-the-box integrations for faster time to value. New integrations include connectors to New Relic Insights, Microsoft Teams, and proxy support for all polling integrations (e.g. Zenoss, Zabbix, vCenter, vSphere, Solarwinds, Spectrum, and SevOne).

“AIOps is gaining momentum streamlining IT Operations as well as DevOps,” explains Phil Tee, Chairman and CEO of Moogsoft. “We’ve built the AIOps market from the beginning, pioneered the way with over 50 patents, and now help over 120 of the largest corporations transform their IT service assurance. Today we’re delivering the next-generation platform to democratize the use of AIOps at all organizations. Our goal is to make Moogsoft the solution of choice for all enterprises – large and small – for agile, proactive event resolution. To this end, release 7.2 empowers enterprises to avoid outages, meet service level agreements, and accelerate digital transformation.”

The Latest

In the world of digital-first business, there is no tolerance for service outages. Businesses know that outages are the quickest way to lose money and customers. For smaller organizations, unplanned downtime could even force the business to close ... A new study from PagerDuty, The State of AI-First Operations, reveals that companies actively incorporating AI into operations now view operational resilience as a growth driver rather than a cost center. But how are they achieving it? ...

In live financial environments, capital markets software cannot pause for rebuilds. New capabilities are introduced as stacked technology layers to meet evolving demands while systems remain active, data keeps moving, and controls stay intact. AI is no exception, and its opportunities are significant: accelerated decision cycles, compressed manual workflows, and more effective operations across complex environments. The constraint isn't the models themselves, but the architectural environments they enter ...

Like most digital transformation shifts, organizations often prioritize productivity and leave security and observability to keep pace. This usually translates to both the mass implementation of new technology and fragmented monitoring and observability (M&O) tooling. In the era of AI and varied cloud architecture, a disparate observability function can be dangerous. IT teams will lack a complete picture of their IT environment, making it harder to diagnose issues while slowing down mean time to resolve (MTTR). In fact, according to recent data from the SolarWinds State of Monitoring & Observability Report, 77% of IT personnel said the lack of visibility across their on-prem and cloud architecture was an issue ...

In MEAN TIME TO INSIGHT Episode 23, Shamus McGillicuddy, VP of Research, Network Infrastructure and Operations, at EMA discusses the NetOps labor shortage ... 

Technology management is evolving, and in turn, so is the scope of FinOps. The FinOps Foundation recently updated their mission statement from "advancing the people who manage the value of cloud" to "advancing the people who manage the value of technology." This seemingly small change solidifies a larger evolution: FinOps practitioners have organically expanded to be focused on more than just cloud cost optimization. Today, FinOps teams are largely — and quickly — expanding their job descriptions, evolving into a critical function for managing the full value of technology ...

Enterprises are under pressure to scale AI quickly. Yet despite considerable investment, adoption continues to stall. One of the most overlooked reasons is vendor sprawl ... In reality, no organization deliberately sets out to create sprawling vendor ecosystems. More often, complexity accumulates over time through well-intentioned initiatives, such as enterprise-wide digital transformation efforts, point solutions, or decentralized sourcing strategies ...

Nearly every conversation about AI eventually circles back to compute. GPUs dominate the headlines while cloud platforms compete for workloads and model benchmarks drive investment decisions. But underneath that noise, a quieter infrastructure challenge is taking shape. The real bottleneck in enterprise AI is not processing power, it is the ability to store, manage and retrieve the relentless volumes of data that AI systems generate, consume and multiply ...

The 2026 Observability Survey from Grafana Labs paints a vivid picture of an industry maturing fast, where AI is welcomed with careful conditions, SaaS economics are reshaping spending decisions, complexity remains a defining challenge, and open standards continue to underpin it all ...

The observability industry has an evolving relationship with AI. We're not skeptics, but it's clear that trust in AI must be earned ... In Grafana Labs' annual Observability Survey, 92% said they see real value in AI surfacing anomalies before they cause downtime. Another 91% endorsed AI for forecasting and root cause analysis. So while the demand is there, customers need it to be trustworthy, as the survey also found that the practitioners most enthusiastic about AI are also the most insistent on explainability ...

In the modern enterprise, the conversation around AI has moved past skepticism toward a stage of active adoption. According to our 2026 State of IT Trends Report: The Human Side of Autonomous AI, nearly 90% of IT professionals view AI as a net positive, and this optimism is well-founded. We are seeing agentic AI move beyond simple automation to actively streamlining complex data insights and eliminating the manual toil that has long hindered innovation. However, as we integrate these autonomous agents into our ecosystems, the fundamental DNA of the IT role is evolving ...